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Buyers' Guide to Content- and Context-Aware Encoding 2018
A new generation of encoders looks at the context of content to deliver better video playback and higher efficiency. Here's what publishers need to know about CAE.

As with all machine-learning solutions, including those with artificial intelligence (AI) algorithms, the early progress made in bitrate ladder enhancements will give way to more complex encoding parameters.

There’s still room for improvement, of course, including options to use different encoding parameters for different scenes in a title, as well as options to determine how many rungs are needed on the bitrate ladder for a given title. Yet the CAE approach is steadily progressing forward as a viable means to take the burden of more mundane compression decisioning off the shoulders of compressionists and content owners.

What Am I Buying?

When trying to differentiate between various CAE-enabled encoding solutions, whether hardware appliances or online encoding services, here are a few key pointers to consider:

First, as mentioned above, does the CAE solution optimize for both the content type and the delivery context of a set of known OTT operators? This could vary from devices and intended uses to known peering arrangements used to load-balance content delivery at scale.

Second, does the CAE solution you’re considering lean more toward saving bandwidth or increasing visual quality? These are the two distinct ways that CAE can be used to create a better viewing experience. One maintains equivalent quality while simultaneously lowering the overall and average bandwidths by 15–20 percent, which means buffering is less likely and content may be able to be delivered to a wider audience if average bandwidths are significantly lowered. The other approach continues to use the same amount of bandwidth previously assigned to a rung of the bitrate ladder, but offers an opportunity to dramatically increase the quality of the visual image at that “standard” bandwidth.

Third, as a byproduct of the points above, does the CAE solution you’re considering save bandwidth and storage, but inadvertently increase the transcoding costs by failing to reduce the number of rungs on the bitrate ladder?

Fourth—and this is key—does the CAE solution break basic encoding parameters such as variable bitrate (VBR) or Group of Pictures (GoP) in achieving bandwidth savings or bitrate-ladder rung reductions? Enabling maximum reach also means maintaining 100 percent compliance with existing media players and devices. The benefit for CAE solutions, whether they be on-prem or cloud-based processes, is the fact that one compression expert’s learnings can be fine-tuned and perhaps even automated in a way that’s beneficial to thousands of encoding sessions via firmware-upgradeable algorithms in an on-prem encoder or transcoder, as well hundreds of thousands of encoding instances launched in a cloud-based encoding solution.

Finally, as we’ve discussed earlier, one needs to determine whether a particular CAE solution is suitable for a single application or multiple applications. The use of the term “application” here means both key market verticals (e.g., enterprise, media and entertainment, corporate communications) and also mobile operating system apps such as those found on the Google Play store or Apple’s App Store.

Some CAE solutions may be hard-coded with limited options for content and formats, as a way to optimize the pre-encoding analysis that must take place in spatial (intraframe) or temporal (interframe) analysis. Once the main market vertical for a particular CAE solution is determined and tested, don’t forget to test for versatility: is the solution versatile enough to handle content encoding parameters for lower-end SD, 720p and 1080p HD, and even up to 4K Ultra HD (UHD)?

Conclusion

As opposed to content-aware encoding, context-aware encoding takes into account both the type of content (e.g., action, sports, screen capture) and the intended playback device, with some context-aware solutions also refining encoding parameters on an OVP- or CDN-specific basis.

Context-aware encoding solutions can be purpose-built for a particular content context, or they can start with a baseline that is then tweaked in a highly customizable, scalable, and tuneable way to allow more flexibility across bitrate ladder rungs that consist of an intersection of format, resolution, and data rate.

We’re still in the early days of content- and context-aware encoding offerings, with more solutions set to launch in Q1 and Q2 of 2018. The team at Streaming Media magazine will keep our eyes peeled for CAE solutions that offer unique approaches coupled with consistent bandwidth savings.

[This article appears in the Spring 2018 issue of Streaming Media European Edition.]

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